DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
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Tarek F. Abdelzaher | Lu Su | Shuochao Yao | Yiran Zhao | Aston Zhang | T. Abdelzaher | Shuochao Yao | Yiran Zhao | Lu Su | Aston Zhang
[1] Yunhuai Liu,et al. CorLayer: a transparent link correlation layer for energy efficient broadcast , 2013, MobiCom.
[2] Richard P. Martin,et al. Detecting driver phone use leveraging car speakers , 2011, MobiCom.
[3] Rui Peng,et al. Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures , 2016, ArXiv.
[4] Stefan Schaal,et al. Policy Gradient Methods for Robotics , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[5] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[6] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[7] Hwee Pink Tan,et al. Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications , 2014, IEEE Communications Surveys & Tutorials.
[8] Ryan J. Halter,et al. Amulet: An Energy-Efficient, Multi-Application Wearable Platform , 2016, SenSys.
[9] Mikkel Baun Kjærgaard,et al. Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition , 2015, SenSys.
[10] Xiaogang Wang,et al. Convolutional neural networks with low-rank regularization , 2015, ICLR.
[11] Zoubin Ghahramani,et al. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.
[12] John N. Tsitsiklis,et al. Actor-Critic Algorithms , 1999, NIPS.
[13] Lothar Thiele,et al. Low-power wireless bus , 2012, SenSys '12.
[14] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[15] Nicholas D. Lane,et al. DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
[16] Mahesh K. Marina,et al. Towards multimodal deep learning for activity recognition on mobile devices , 2016, UbiComp Adjunct.
[17] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[18] Yurong Chen,et al. Dynamic Network Surgery for Efficient DNNs , 2016, NIPS.
[19] Peter W. Glynn,et al. Likelihood ratio gradient estimation for stochastic systems , 1990, CACM.
[20] Agathoniki Trigoni,et al. Leveraging User Activities and Mobile Robots for Semantic Mapping and User Localization , 2017, HRI.
[21] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[22] Dacheng Tao,et al. Packing Convolutional Neural Networks in the Frequency Domain , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Gaurav S. Sukhatme,et al. OCRdroid: A Framework to Digitize Text Using Mobile Phones , 2009, MobiCASE.
[24] Aren Jansen,et al. Audio Set: An ontology and human-labeled dataset for audio events , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[25] Zoubin Ghahramani,et al. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference , 2015, ArXiv.
[26] Sen Wang,et al. VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem , 2017, AAAI.
[27] Shaowen Wang,et al. Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning , 2017, WWW.
[28] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, NIPS.
[29] Pritish Narayanan,et al. Deep Learning with Limited Numerical Precision , 2015, ICML.
[30] Marco Zuniga,et al. Incremental Wi-Fi scanning for energy-efficient localization , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[31] Kay Römer,et al. A Competition to Push the Dependability of Low-Power Wireless Protocols to the Edge , 2017, EWSN.
[32] Sanjeev Khudanpur,et al. Librispeech: An ASR corpus based on public domain audio books , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[33] Sergey Levine,et al. MuProp: Unbiased Backpropagation for Stochastic Neural Networks , 2015, ICLR.
[34] Nicholas D. Lane,et al. Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables , 2016, SenSys.
[35] Cecilia Mascolo,et al. CARISMA: Context-Aware Reflective mIddleware System for Mobile Applications , 2003, IEEE Trans. Software Eng..
[36] Pei Zhang,et al. PANDAA: physical arrangement detection of networked devices through ambient-sound awareness , 2011, UbiComp '11.
[37] Bhaskar Krishnamachari,et al. Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks , 2018, IEEE Transactions on Cognitive Communications and Networking.
[38] Nectarios Koziris,et al. Understanding the Performance of Sparse Matrix-Vector Multiplication , 2008, 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008).
[39] J. Tsitsiklis,et al. Actor-citic agorithms , 1999, NIPS 1999.
[40] Xenofon D. Koutsoukos,et al. Tracking mobile nodes using RF Doppler shifts , 2007, SenSys '07.
[41] Leonidas J. Guibas,et al. Inferring mobile trajectories using a network of binary proximity sensors , 2011, 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.
[42] Shaohan Hu,et al. DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing , 2016, WWW.
[43] Navdeep Jaitly,et al. Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.
[44] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[45] S. Bhattacharya,et al. Sparsifying Deep Learning Layers for Constrained Resource Inference on Wearables , 2016 .
[46] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[47] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[48] Sen Wang,et al. DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[49] Joan Bruna,et al. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.
[50] Charu C. Aggarwal,et al. Recursive Ground Truth Estimator for Social Data Streams , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
[51] Ranveer Chandra,et al. SNOW: Sensor Network over White Spaces , 2016, SenSys.
[52] Neal Patwari,et al. See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks , 2011, IEEE Transactions on Mobile Computing.
[53] Yoshua Bengio,et al. Training deep neural networks with low precision multiplications , 2014 .
[54] Yunhuai Liu,et al. CorLayer: A Transparent Link Correlation Layer for Energy-Efficient Broadcast , 2015, IEEE/ACM Transactions on Networking.
[55] Shangxing Wang,et al. Deep Reinforcement Learning for Dynamic Multichannel Access , 2017 .
[56] Anthony Rowe,et al. Contactless sensing of appliance state transitions through variations in electromagnetic fields , 2010, BuildSys '10.
[57] Wojciech Zaremba,et al. Recurrent Neural Network Regularization , 2014, ArXiv.
[58] Qiang Li,et al. MusicalHeart: a hearty way of listening to music , 2012, SenSys '12.
[59] Wen Hu,et al. Face recognition on smartphones via optimised Sparse Representation Classification , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.
[60] Enamul Hoque,et al. Vocal-Diary: A Voice Command based Ground Truth Collection System for Activity Recognition , 2014, Wireless Health.
[61] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[62] Nicholas D. Lane,et al. DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning , 2015, UbiComp.
[63] Ming Yang,et al. Compressing Deep Convolutional Networks using Vector Quantization , 2014, ArXiv.